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Semiparametric surveillance of outbreaks

Abstract
The detection of a change from a constant level to a monotonically increasing (or decreasing) regression is of special interest for the detection of outbreaks of, for example, epidemics. A maximum likelihood ratio statistic for the sequential surveillance of an “outbreak” situation is derived. The method is semiparametric in the sense that the regression model is nonparametric while the distribution belongs to the regular exponential family. The method is evaluated with respect to timeliness and predicted value in a simulation study that imitates the influenza outbreaks in Sweden. To illustrate its performance, the method is applied to Swedish influenza data for six years. The advantage of this semiparametric surveillance method, which does not rely on an estimated baseline, is illustrated by a Monte Carlo study. The proposed method is successively accumulating the information. Such accumulation is not made by the commonly used approach where the current observation is compared to a baseline. The advantage of information accumulation is illustrated.
URI
http://hdl.handle.net/2077/10527
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  • Research Report
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gupea_2077_10527_1.pdf (386.7Kb)
Date
2008-07-02
Author
Frisén, Marianne
Andersson, Eva
Keywords
Monitoring
Change-points
Generalised likelihood
Ordered regression
Robust regression
Exponential family
Publication type
report
ISSN
Semiparametric surveillance of outbreaks
Series/Report no.
Research Report
2007:11
Language
eng
Metadata
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